Deep Learning - Artificial Neural Networks with Tensorflow - Categorical Cross Entropy

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Computers
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11th Grade - University
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary use of the cross entropy loss function in machine learning?
To calculate the mean squared error
To optimize binary classification models
To evaluate multi-class classification models
To measure the accuracy of a model
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which distribution is used for modeling multiple categorical outcomes?
Bernoulli distribution
Poisson distribution
Categorical distribution
Gaussian distribution
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the indicator function return when its argument is true?
Zero
One
The argument itself
A random value
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is one-hot encoding considered inefficient?
It requires more memory
It increases computational complexity
It does not work with categorical data
It is difficult to implement
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main advantage of using sparse categorical cross entropy over regular categorical cross entropy?
It is easier to understand
It supports more data types
It is more accurate
It requires fewer computations
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does numpy's double indexing help in implementing sparse categorical cross entropy?
It reduces memory usage
It increases the speed of computation
It allows direct indexing without one-hot encoding
It simplifies the code
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In TensorFlow, what does using sparse categorical cross entropy allow you to avoid?
Using one-hot encoded targets
Calculating gradients
Training the model
Using large datasets
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